Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques

Predictive analytics, a division of the advanced analytics that uses various techniques like machine learning, data mining and so on, to predict the future events. Predictive analytics is summarized with the data collection, modelling, statistics and deployment. It can be used to predict the future possibilities in different areas like business, healthcare, telecom, finance. An effective technique for prediction is Artificial Neural Network. The model accuracy for prediction can be enhanced using neural networks. The model can also be used easily for prediction of output parameters because of its ability to solve the complex computation which are difficult to be solved by other techniques. In this paper, a brief review of Artificial Neural Network used for prediction analysis is presented with various techniques like Multi-Layer Perceptron, T-S Fuzzy Neural Networks, Support Vector Machine, Radial Basis Function Network, Levenberg-Marquardt Algorithm and Back Propagation and their applications are also presented. This paper also presents the neural network-based prediction model for job applicants which is used to predict the jobs of various applicants based on certain parameter ratings.

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A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

Energies ◽

10.3390/en12183454 ◽

2019 ◽

Vol 12 (18) ◽

pp. 3454 ◽

Cited By ~ 5

Author(s):

Adolfo Crespo Márquez ◽

Antonio de la Fuente Carmona ◽

Sara Antomarioni

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Association Rule ◽

Performance Monitoring ◽

Predictive Analytics ◽

Practical Implementation ◽

Operational Conditions ◽

Important Concern ◽

Artificial Neural ◽

Artificial Neural Network Ann

In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption efficiency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence differs from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.

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